The invention relates generally to radar filtering. In particular, the invention relates to discrimination from clutter of received radar signals by distinguishing antenna patterns. Such clutter from low-velocity sources can obscure the target, compensation for this effect being the inventive focus.
Radar systems employ Doppler processing to discriminate targets from clutter. This process operates satisfactorily for targets having high Doppler frequency that contrast with clutter typically having zero or low Doppler frequency. However, this does not hold for Doppler frequencies close to the clutter. Detection of slow moving targets necessitate having an accurate estimate of the clutter's Doppler spectrum because the detection process endeavors to filter out the clutter power based on background Doppler spectrum. Clutter presents undesirable radar return signals and thereby constitutes noise.
Any error in the knowledge of the clutter Doppler spectrum degrades the detector's ability to distinguish targets. This degradation is negligible for fast moving targets but can be quite significant for targets whose Doppler frequency approaches the clutter Doppler spectrum. The classic approach to the problem of determining the clutter spectrum for the benefit of improved target detection is some sort of on-line clutter estimation scheme coupled with a detector.
There have been many technical papers that incorporate this approach by estimating the clutter spectrum and including this information in their detector structure, such as R. S. Raghavan, “Statistical Interpretation of a Data Adaptive Clutter Subspace Estimation Algorithm”, IEEE Transactions on Aerospace and Electronic Systems, 48 (2), 1370-1384 (April 2012); Peng-Lang Shui, Yan-Ling Shi, “Subband ANMF Detection of Moving Targets in Sea Clutter”, IEEE Transactions on Aerospace and Electronic Systems, 48 (4), 3578-3593 (October 2012). However, these approaches are complicated and must compensate for the non-stationarity of clutter.
This leads to the problem of obtaining sufficient training data, while taking into account real world issues of said training data being corrupted due to radio frequency interference (RFI). The exemplary approach described in the disclosure enables the radar designer to estimate the clutter spectrum accurately using knowledge of the antenna pattern alone. Using the accurate estimate of the clutter spectrum enables providing an optimum filter with the addition of an estimate of clutter-to-noise ratio (CNR), which can be accurately measured on-line or estimated with a clutter model.
Exemplary embodiments improve weather prediction using radar. Weather radars produce the three weather determinations based on analysis of clutter, as described by D. J. Doviak, et al., Doppler Radar and Weather Observations 2nd edition, Academic Press (1993). These are: (1) Weather signal power of the zeroth moment of the Doppler spectrum. (2) Mean Doppler velocity of the first moment of the power-normalized spectra. (3) Spectrum width, the square root of the second moment about the first of the normalized spectrum. This is a measure of the velocity dispersion within the resolution volume.
Clutter can seriously degrade the accuracy of the weather moments produced by weather radars. The largest amplitude clutter that weather radars must contend with is ground clutter. The classic approach to the problem of clutter in weather radar involves filtering out the clutter using Doppler processing. These schemes rely on assumptions of the clutter correlation matrix or Doppler spectrum. (Correlation and spectrum are both related by the Fourier transform. Thus, knowing one enables computing the other.)